Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.

Details

Serval ID
serval:BIB_78334C110212
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.
Journal
Nature communications
Author(s)
Rosenthal G., Váša F., Griffa A., Hagmann P., Amico E., Goñi J., Avidan G., Sporns O.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
05/06/2018
Peer-reviewed
Oui
Volume
9
Number
1
Pages
2178
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Abstract
Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.
Keywords
Brain/diagnostic imaging, Brain/physiology, Computer Simulation, Connectome/methods, Diffusion Tensor Imaging/methods, Humans, Image Processing, Computer-Assisted, Models, Neurological, Nerve Net/diagnostic imaging, Nerve Net/physiology, Rotation
Pubmed
Web of science
Open Access
Yes
Create date
15/06/2018 16:21
Last modification date
20/08/2019 14:34
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